Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation

dc.authorid0000-0002-3712-6534en_US
dc.authorid0000-0002-5377-7249en_US
dc.authorid0000-0002-4099-1254en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0002-9087-3010en_US
dc.authorid0000-0003-1840-9958en_US
dc.contributor.authorSrivastava, Rohini
dc.contributor.authorKumar, Basant
dc.contributor.authorAlenezi, Fayadh
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorAlthubiti, Sara A.
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-10-31T06:37:40Z
dc.date.available2023-10-31T06:37:40Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThis paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.en_US
dc.identifier.citationSrivastava, R., Kumar, B., Alenezi, F., Alhudhaif, A., Althubiti, S. A., & Polat, K. (2022). Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation. Mathematical Problems in Engineering, 2022, 1-11.en_US
dc.identifier.doi10.1155/2022/7564036
dc.identifier.endpage11en_US
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85128243135en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/7564036
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11800
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000807345500013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofMathematical Problems in Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHardware Implementationen_US
dc.subjectClassificationen_US
dc.subjectAlgorithmen_US
dc.titleAutomatic arrhythmia detection based on the probabilistic neural network with FPGA implementationen_US
dc.typeArticleen_US

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